# Decision Trees - Machine Learning

Essay by   •  April 7, 2017  •  Coursework  •  2,211 Words (9 Pages)  •  455 Views

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Customer Loyalty Assessment based on Demographics

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 Model Summary Decision Trees – CHAID Approach Response & Predictors Specifications Growing Method CHAID Is the tree model a good predictive model for High Loyalty Customers? Name Type Values Dependent Variable BINARY LOYALTY Loyalty Ordinal 0-LOW Independent Variables AGE, GENDER, 1-HIGH EDUCATION, MARITAL 1:18-24 STATUS, INCOME 2:25-34 Age Ordinal 3:35-44 4:45-54 5:55+years Validation None 1-MALE Gender Ordinal Maximum Tree Depth 3 2-FEMALE Minimum Cases in 25 1-High School 2- Some College Education Ordinal Parent Node 3-College 4-Graduate School Minimum Cases in 15 1-Married Marital Child Node Ordinal 2-Single Status 3-Divorced/Separated 4- Results Independent Variables GENDER Widow/Widower Included 1:<35K Number of Nodes 3 2:35-54.999K 3:55-74.999K Number of Terminal 2 Income Ordinal 4:75-94.999K 5-95-114.999K Nodes 6:115-134.999K 7:>135K Depth 1

Customer Loyalty Assessment based on Demographics

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• Among predictors i.e. the demographic variables - Assessing based on the CHAID Analysis yields that only Gender is the predictor that gives a Chi Square value having statistical significance < .05 or 5% for the relation of its 2 categories with response variable 'Loyalty'. This means that the process of comparison between 2 categories of a predictor, repeated for each of the predictors merging the categories found not to have statistically significant relationship with response variable in earlier comparisons did not have any 2 categories of a predictor having a significant effect on loyalty of the customer for the brand except for Male and Female categories of Gender that show significant affect on customer loyalty.
• As per the CHAID algorithm, the categories for Ordinal Variables are naturally defined and for continuous variables are taken in a bucket of 10 units(such as for Age).

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Customer Loyalty Assessment based on Demographics

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